Dear helpeRs,
I'm estimating a series of linear models (using lm) in which in every
new model variables are added. I want to test to what degree the new
variables can explain the effects of the variables already present in
the models. In order to do that, I simply observe wether these
effects decrease in strength and / or lose their significance.
My question is: does any of you know a package / function in R that
can test whether these changes in effects between models are
significant? I figure these effects follow a T-distribution and I
know the std. devs., so it must be easy to do manually. But I would
like not to invent the wheel, when the function is already present.
Below is an example of what I mean. In model2, the variable z is
added, which is hypothesized to partly explain the effect of x.
Indeed, the effect of x decreases in model2, compared to model1. What
I want to find out, is if this decrease is statistically significant.
Many thanks,
Rense
x <- c(1,1,1,1,1,2,2,2,2,2,3,4,4,4,5)
z <- c(2,2,2,2,2,2,2,2,3,3,3,3,4,4,5)
y <- c(1,2,2,2,3,3,3,3,4,4,4,5,5,5,5)
model1 <- lm(y~x)
model2 <- lm(y~x+z)
[[alternative HTML version deleted]]